{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 日期时间索引和时间序列\n",
    "\n",
    "## 学习目标\n",
    "- 理解 DatetimeIndex 的概念和创建方法\n",
    "- 掌握使用 DatetimeIndex 创建时间序列数据\n",
    "- 学会时间序列的索引、切片和查询方法\n",
    "- 掌握时间序列的重新索引、对齐和填充技术\n",
    "- 了解时间序列的可视化方法\n",
    "- 通过实际案例巩固知识点\n",
    "\n",
    "## 核心知识点\n",
    "1. **DatetimeIndex**：时间序列的索引类型，提供强大的时间查询能力\n",
    "2. **时间序列创建**：使用 DatetimeIndex 创建 Series 和 DataFrame\n",
    "3. **时间索引**：按日期、月份、年份等灵活索引和切片\n",
    "4. **重新索引**：处理不规则时间序列，填充缺失值\n",
    "5. **时间对齐**：不同时间序列的自动对齐和合并\n",
    "6. **实际应用**：股票数据、传感器数据等场景"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第一部分：DatetimeIndex 基础\n",
    "\n",
    "### 1.1 什么是 DatetimeIndex\n",
    "\n",
    "`DatetimeIndex` 是 Pandas 中专门用于时间序列数据的索引类型。它提供了强大的时间查询、切片和操作能力。\n",
    "\n",
    "**主要特点**：\n",
    "- 支持灵活的时间索引和切片\n",
    "- 自动识别时间频率\n",
    "- 支持时区处理\n",
    "- 提供丰富的时间相关属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "方法1: 指定起始日期和数量\n",
      "DatetimeIndex(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04',\n",
      "               '2023-01-05', '2023-01-06', '2023-01-07', '2023-01-08',\n",
      "               '2023-01-09', '2023-01-10'],\n",
      "              dtype='datetime64[ns]', freq='D')\n",
      "类型: <class 'pandas.core.indexes.datetimes.DatetimeIndex'>\n",
      "频率: <Day>\n",
      "时区: None\n",
      "\n",
      "方法2: 指定起始和结束日期\n",
      "DatetimeIndex(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04',\n",
      "               '2023-01-05', '2023-01-06', '2023-01-07', '2023-01-08',\n",
      "               '2023-01-09', '2023-01-10'],\n",
      "              dtype='datetime64[ns]', freq='D')\n",
      "\n",
      "方法3: 从字符串列表创建\n",
      "DatetimeIndex(['2023-01-01', '2023-01-15', '2023-02-01'], dtype='datetime64[ns]', freq=None)\n",
      "类型: <class 'pandas.core.indexes.datetimes.DatetimeIndex'>\n",
      "频率: None (不规则序列)\n"
     ]
    }
   ],
   "source": [
    "# 方法1: 使用 start 和 periods 参数\n",
    "dates1 = pd.date_range('2023-01-01', periods=10, freq='D')\n",
    "print(\"方法1: 指定起始日期和数量\")\n",
    "print(dates1)\n",
    "print(f\"类型: {type(dates1)}\")\n",
    "print(f\"频率: {dates1.freq}\")\n",
    "print(f\"时区: {dates1.tz}\")\n",
    "print()\n",
    "\n",
    "# 方法2: 使用 start 和 end 参数\n",
    "dates2 = pd.date_range('2023-01-01', '2023-01-10', freq='D')\n",
    "print(\"方法2: 指定起始和结束日期\")\n",
    "print(dates2)\n",
    "print()\n",
    "\n",
    "# 方法3: 从字符串列表创建\n",
    "date_list = ['2023-01-01', '2023-01-15', '2023-02-01']\n",
    "dt_index = pd.DatetimeIndex(date_list)\n",
    "print(\"方法3: 从字符串列表创建\")\n",
    "print(dt_index)\n",
    "print(f\"类型: {type(dt_index)}\")\n",
    "print(f\"频率: {dt_index.freq} (不规则序列)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 DatetimeIndex 的常用属性\n",
    "\n",
    "DatetimeIndex 提供了丰富的属性来获取时间信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex 常用属性:\n",
      "============================================================\n",
      "索引值: [Timestamp('2023-01-01 00:00:00'), Timestamp('2023-01-02 00:00:00'), Timestamp('2023-01-03 00:00:00')] ...\n",
      "长度: 10\n",
      "起始日期: 2023-01-01 00:00:00\n",
      "结束日期: 2023-01-10 00:00:00\n",
      "频率: <Day>\n",
      "时区: None\n",
      "是否为单调递增: True\n",
      "是否为唯一: True\n"
     ]
    }
   ],
   "source": [
    "# 创建示例 DatetimeIndex\n",
    "dates = pd.date_range('2023-01-01', periods=10, freq='D')\n",
    "\n",
    "print(\"DatetimeIndex 常用属性:\")\n",
    "print(\"=\" * 60)\n",
    "print(f\"索引值: {dates[:3].tolist()} ...\")\n",
    "print(f\"长度: {len(dates)}\")\n",
    "print(f\"起始日期: {dates.min()}\")\n",
    "print(f\"结束日期: {dates.max()}\")\n",
    "print(f\"频率: {dates.freq}\")\n",
    "print(f\"时区: {dates.tz}\")\n",
    "print(f\"是否为单调递增: {dates.is_monotonic_increasing}\")\n",
    "print(f\"是否为唯一: {dates.is_unique}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "时间序列:\n",
      "2023-01-01    0.429657\n",
      "2023-01-02    1.323414\n",
      "2023-01-03   -0.587333\n",
      "2023-01-04   -0.462401\n",
      "2023-01-05   -0.061028\n",
      "2023-01-06    1.468129\n",
      "2023-01-07   -0.931994\n",
      "2023-01-08   -1.176914\n",
      "2023-01-09   -0.950097\n",
      "2023-01-10   -1.590839\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "索引类型: <class 'pandas.core.indexes.datetimes.DatetimeIndex'>\n",
      "\n",
      "时间序列DataFrame:\n",
      "              value1    value2\n",
      "2023-01-01 -1.246720 -0.372244\n",
      "2023-01-02 -0.833616 -1.492341\n",
      "2023-01-03 -2.740914 -1.013505\n",
      "2023-01-04 -2.648492 -2.267494\n",
      "2023-01-05 -2.477951 -4.401098\n"
     ]
    }
   ],
   "source": [
    "# 创建时间序列Series\n",
    "dates = pd.date_range('2023-01-01', periods=10, freq='D')\n",
    "values = np.random.randn(10).cumsum()\n",
    "ts = pd.Series(values, index=dates)\n",
    "\n",
    "print(\"时间序列:\")\n",
    "print(ts)\n",
    "print(f\"\\n索引类型: {type(ts.index)}\")\n",
    "\n",
    "# 创建时间序列DataFrame\n",
    "df = pd.DataFrame({\n",
    "    'value1': np.random.randn(10).cumsum(),\n",
    "    'value2': np.random.randn(10).cumsum()\n",
    "}, index=dates)\n",
    "\n",
    "print(\"\\n时间序列DataFrame:\")\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 时间序列索引和切片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 创建时间序列 DataFrame\n",
    "\n",
    "DataFrame 也可以使用 DatetimeIndex 作为行索引，创建多列时间序列数据。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "时间序列 DataFrame:\n",
      "              value1    value2    value3\n",
      "2023-01-01  0.367385 -0.230921  1.165277\n",
      "2023-01-02  0.602016 -0.579047  2.782516\n",
      "2023-01-03  2.082406 -0.059388  0.912000\n",
      "2023-01-04  3.755179  0.572349  0.400805\n",
      "2023-01-05  3.919751  0.573397 -0.599806\n",
      "2023-01-06  2.372562  0.208885 -1.910390\n",
      "2023-01-07  3.567858 -0.021303 -3.654601\n",
      "2023-01-08  3.372247 -1.401741 -5.126518\n",
      "2023-01-09  2.650501 -1.213114 -5.802359\n",
      "2023-01-10  1.610802 -2.379391 -6.284662\n",
      "\n",
      "索引类型: <class 'pandas.core.indexes.datetimes.DatetimeIndex'>\n",
      "列名: ['value1', 'value2', 'value3']\n",
      "数据形状: (10, 3)\n"
     ]
    }
   ],
   "source": [
    "# 创建时间序列 DataFrame\n",
    "dates = pd.date_range('2023-01-01', periods=10, freq='D')\n",
    "df = pd.DataFrame({\n",
    "    'value1': np.random.randn(10).cumsum(),\n",
    "    'value2': np.random.randn(10).cumsum(),\n",
    "    'value3': np.random.randn(10).cumsum()\n",
    "}, index=dates)\n",
    "\n",
    "print(\"时间序列 DataFrame:\")\n",
    "print(df)\n",
    "print()\n",
    "print(f\"索引类型: {type(df.index)}\")\n",
    "print(f\"列名: {df.columns.tolist()}\")\n",
    "print(f\"数据形状: {df.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特定日期:\n",
      "-1.6559640599953855\n",
      "\n",
      "2023年1月:\n",
      "2023-01-01   -1.655964\n",
      "2023-01-02   -0.548360\n",
      "2023-01-03    0.589151\n",
      "2023-01-04    2.237636\n",
      "2023-01-05    0.885232\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "1月1日到1月10日:\n",
      "2023-01-01   -1.655964\n",
      "2023-01-02   -0.548360\n",
      "2023-01-03    0.589151\n",
      "2023-01-04    2.237636\n",
      "2023-01-05    0.885232\n",
      "2023-01-06    0.686547\n",
      "2023-01-07    1.603879\n",
      "2023-01-08    0.287746\n",
      "2023-01-09   -0.501833\n",
      "2023-01-10    0.605015\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "2023年前5天:\n",
      "2023-01-01   -1.655964\n",
      "2023-01-02   -0.548360\n",
      "2023-01-03    0.589151\n",
      "2023-01-04    2.237636\n",
      "2023-01-05    0.885232\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 创建更长的时间序列\n",
    "dates = pd.date_range('2023-01-01', '2023-12-31', freq='D')\n",
    "ts = pd.Series(np.random.randn(len(dates)).cumsum(), index=dates)\n",
    "\n",
    "# 按日期索引\n",
    "print(\"特定日期:\")\n",
    "print(ts['2023-01-01'])\n",
    "\n",
    "# 按月份索引\n",
    "print(\"\\n2023年1月:\")\n",
    "print(ts['2023-01'].head())\n",
    "\n",
    "# 日期范围切片\n",
    "print(\"\\n1月1日到1月10日:\")\n",
    "print(ts['2023-01-01':'2023-01-10'])\n",
    "\n",
    "# 部分字符串索引\n",
    "print(\"\\n2023年前5天:\")\n",
    "print(ts['2023'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用loc按日期:\n",
      "2023-01-01   -1.655964\n",
      "2023-01-02   -0.548360\n",
      "2023-01-03    0.589151\n",
      "2023-01-04    2.237636\n",
      "2023-01-05    0.885232\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "使用iloc按位置:\n",
      "2023-01-01   -1.655964\n",
      "2023-01-02   -0.548360\n",
      "2023-01-03    0.589151\n",
      "2023-01-04    2.237636\n",
      "2023-01-05    0.885232\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "正值数据:\n",
      "2023-01-03    0.589151\n",
      "2023-01-04    2.237636\n",
      "2023-01-05    0.885232\n",
      "2023-01-06    0.686547\n",
      "2023-01-07    1.603879\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 使用loc和iloc\n",
    "print(\"使用loc按日期:\")\n",
    "print(ts.loc['2023-01-01':'2023-01-05'])\n",
    "\n",
    "print(\"\\n使用iloc按位置:\")\n",
    "print(ts.iloc[0:5])\n",
    "\n",
    "# 布尔索引\n",
    "print(\"\\n正值数据:\")\n",
    "positive_values = ts[ts > 0]\n",
    "print(positive_values.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 时间序列重新索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4. 日期范围切片:\n",
      "------------------------------------------------------------\n",
      "1月1日到1月10日:\n",
      "2023-01-01   -1.655964\n",
      "2023-01-02   -0.548360\n",
      "2023-01-03    0.589151\n",
      "2023-01-04    2.237636\n",
      "2023-01-05    0.885232\n",
      "2023-01-06    0.686547\n",
      "2023-01-07    1.603879\n",
      "2023-01-08    0.287746\n",
      "2023-01-09   -0.501833\n",
      "2023-01-10    0.605015\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "5. 使用 loc 进行范围切片:\n",
      "------------------------------------------------------------\n",
      "1月1日到1月5日:\n",
      "2023-01-01   -1.655964\n",
      "2023-01-02   -0.548360\n",
      "2023-01-03    0.589151\n",
      "2023-01-04    2.237636\n",
      "2023-01-05    0.885232\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "6. 使用 iloc 按位置索引:\n",
      "------------------------------------------------------------\n",
      "前5条数据:\n",
      "2023-01-01   -1.655964\n",
      "2023-01-02   -0.548360\n",
      "2023-01-03    0.589151\n",
      "2023-01-04    2.237636\n",
      "2023-01-05    0.885232\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "7. 布尔索引（筛选正值）:\n",
      "------------------------------------------------------------\n",
      "正值数据数量: 106\n",
      "前5个正值:\n",
      "2023-01-03    0.589151\n",
      "2023-01-04    2.237636\n",
      "2023-01-05    0.885232\n",
      "2023-01-06    0.686547\n",
      "2023-01-07    1.603879\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 日期范围切片\n",
    "print(\"4. 日期范围切片:\")\n",
    "print(\"-\" * 60)\n",
    "print(\"1月1日到1月10日:\")\n",
    "print(ts['2023-01-01':'2023-01-10'])\n",
    "print()\n",
    "\n",
    "# 使用 loc 进行范围切片\n",
    "print(\"5. 使用 loc 进行范围切片:\")\n",
    "print(\"-\" * 60)\n",
    "print(\"1月1日到1月5日:\")\n",
    "print(ts.loc['2023-01-01':'2023-01-05'])\n",
    "print()\n",
    "\n",
    "# 使用 iloc 按位置索引\n",
    "print(\"6. 使用 iloc 按位置索引:\")\n",
    "print(\"-\" * 60)\n",
    "print(\"前5条数据:\")\n",
    "print(ts.iloc[0:5])\n",
    "print()\n",
    "\n",
    "# 布尔索引\n",
    "print(\"7. 布尔索引（筛选正值）:\")\n",
    "print(\"-\" * 60)\n",
    "positive_values = ts[ts > 0]\n",
    "print(f\"正值数据数量: {len(positive_values)}\")\n",
    "print(\"前5个正值:\")\n",
    "print(positive_values.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始不规则时间序列:\n",
      "2023-01-01    10\n",
      "2023-01-03    20\n",
      "2023-01-07    30\n",
      "2023-01-10    40\n",
      "dtype: int64\n",
      "\n",
      "重新索引到每日（缺失值用NaN填充）:\n",
      "2023-01-01    10.0\n",
      "2023-01-02     NaN\n",
      "2023-01-03    20.0\n",
      "2023-01-04     NaN\n",
      "2023-01-05     NaN\n",
      "2023-01-06     NaN\n",
      "2023-01-07    30.0\n",
      "2023-01-08     NaN\n",
      "2023-01-09     NaN\n",
      "2023-01-10    40.0\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "前向填充（用前一个有效值填充）:\n",
      "2023-01-01    10.0\n",
      "2023-01-02    10.0\n",
      "2023-01-03    20.0\n",
      "2023-01-04    20.0\n",
      "2023-01-05    20.0\n",
      "2023-01-06    20.0\n",
      "2023-01-07    30.0\n",
      "2023-01-08    30.0\n",
      "2023-01-09    30.0\n",
      "2023-01-10    40.0\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "后向填充（用后一个有效值填充）:\n",
      "2023-01-01    10.0\n",
      "2023-01-02    20.0\n",
      "2023-01-03    20.0\n",
      "2023-01-04    30.0\n",
      "2023-01-05    30.0\n",
      "2023-01-06    30.0\n",
      "2023-01-07    30.0\n",
      "2023-01-08    40.0\n",
      "2023-01-09    40.0\n",
      "2023-01-10    40.0\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "线性插值:\n",
      "2023-01-01    10.000000\n",
      "2023-01-02    15.000000\n",
      "2023-01-03    20.000000\n",
      "2023-01-04    22.500000\n",
      "2023-01-05    25.000000\n",
      "2023-01-06    27.500000\n",
      "2023-01-07    30.000000\n",
      "2023-01-08    33.333333\n",
      "2023-01-09    36.666667\n",
      "2023-01-10    40.000000\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 创建不规则时间序列（只有部分日期有数据）\n",
    "irregular_dates = pd.to_datetime(['2023-01-01', '2023-01-03', '2023-01-07', '2023-01-10'])\n",
    "irregular_ts = pd.Series([10, 20, 30, 40], index=irregular_dates)\n",
    "\n",
    "print(\"原始不规则时间序列:\")\n",
    "print(irregular_ts)\n",
    "print()\n",
    "\n",
    "# 重新索引到规则间隔（每日）\n",
    "regular_dates = pd.date_range('2023-01-01', '2023-01-10', freq='D')\n",
    "reindexed = irregular_ts.reindex(regular_dates)\n",
    "\n",
    "print(\"重新索引到每日（缺失值用NaN填充）:\")\n",
    "print(reindexed)\n",
    "print()\n",
    "\n",
    "# 前向填充（forward fill）\n",
    "forward_filled = irregular_ts.reindex(regular_dates).ffill()\n",
    "print(\"前向填充（用前一个有效值填充）:\")\n",
    "print(forward_filled)\n",
    "print()\n",
    "\n",
    "# 后向填充（backward fill）\n",
    "backward_filled = irregular_ts.reindex(regular_dates).bfill()\n",
    "print(\"后向填充（用后一个有效值填充）:\")\n",
    "print(backward_filled)\n",
    "print()\n",
    "\n",
    "# 线性插值\n",
    "interpolated = irregular_ts.reindex(regular_dates).interpolate(method='linear')\n",
    "print(\"线性插值:\")\n",
    "print(interpolated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第五部分：时间序列对齐和合并\n",
    "\n",
    "当对多个时间序列进行运算时，Pandas 会自动根据索引对齐数据。这是时间序列分析的重要特性。\n",
    "\n",
    "### 5.1 自动对齐运算\n",
    "\n",
    "不同时间范围的时间序列进行运算时，会自动对齐到共同的索引。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 填充方法对比\n",
    "\n",
    "不同的填充方法适用于不同的场景：\n",
    "- **前向填充（ffill）**：适用于数据变化缓慢的场景\n",
    "- **后向填充（bfill）**：适用于需要未来值填充的场景\n",
    "- **线性插值（interpolate）**：适用于数据连续变化的场景\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不同填充方法对比:\n",
      "======================================================================\n",
      "             原始值  前向填充  后向填充       线性插值\n",
      "2023-01-01  10.0  10.0  10.0  10.000000\n",
      "2023-01-02   NaN  10.0  20.0  15.000000\n",
      "2023-01-03  20.0  20.0  20.0  20.000000\n",
      "2023-01-04   NaN  20.0  30.0  22.500000\n",
      "2023-01-05   NaN  20.0  30.0  25.000000\n",
      "2023-01-06   NaN  20.0  30.0  27.500000\n",
      "2023-01-07  30.0  30.0  30.0  30.000000\n",
      "2023-01-08   NaN  30.0  40.0  33.333333\n",
      "2023-01-09   NaN  30.0  40.0  36.666667\n",
      "2023-01-10  40.0  40.0  40.0  40.000000\n"
     ]
    }
   ],
   "source": [
    "# 对比不同填充方法\n",
    "comparison = pd.DataFrame({\n",
    "    '原始值': irregular_ts.reindex(regular_dates),\n",
    "    '前向填充': irregular_ts.reindex(regular_dates).ffill(),\n",
    "    '后向填充': irregular_ts.reindex(regular_dates).bfill(),\n",
    "    '线性插值': irregular_ts.reindex(regular_dates).interpolate(method='linear')\n",
    "})\n",
    "\n",
    "print(\"不同填充方法对比:\")\n",
    "print(\"=\" * 70)\n",
    "print(comparison)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "时间序列1（1月1日-1月5日）:\n",
      "2023-01-01    1\n",
      "2023-01-02    2\n",
      "2023-01-03    3\n",
      "2023-01-04    4\n",
      "2023-01-05    5\n",
      "Freq: D, dtype: int64\n",
      "\n",
      "时间序列2（1月3日-1月7日）:\n",
      "2023-01-03    10\n",
      "2023-01-04    20\n",
      "2023-01-05    30\n",
      "2023-01-06    40\n",
      "2023-01-07    50\n",
      "Freq: D, dtype: int64\n",
      "\n",
      "相加结果（自动对齐到共同索引）:\n",
      "2023-01-01     NaN\n",
      "2023-01-02     NaN\n",
      "2023-01-03    13.0\n",
      "2023-01-04    24.0\n",
      "2023-01-05    35.0\n",
      "2023-01-06     NaN\n",
      "2023-01-07     NaN\n",
      "Freq: D, dtype: float64\n",
      "\n",
      "说明：\n",
      "- 1月1日、1月2日：只有ts1有值，结果为NaN\n",
      "- 1月3日-1月5日：两个序列都有值，正常相加\n",
      "- 1月6日、1月7日：只有ts2有值，结果为NaN\n"
     ]
    }
   ],
   "source": [
    "# 创建两个不同时间范围的序列\n",
    "ts1 = pd.Series([1, 2, 3, 4, 5], \n",
    "                index=pd.date_range('2023-01-01', periods=5, freq='D'))\n",
    "ts2 = pd.Series([10, 20, 30, 40, 50], \n",
    "                index=pd.date_range('2023-01-03', periods=5, freq='D'))\n",
    "\n",
    "print(\"时间序列1（1月1日-1月5日）:\")\n",
    "print(ts1)\n",
    "print()\n",
    "print(\"时间序列2（1月3日-1月7日）:\")\n",
    "print(ts2)\n",
    "print()\n",
    "\n",
    "# 自动对齐运算\n",
    "result = ts1 + ts2\n",
    "print(\"相加结果（自动对齐到共同索引）:\")\n",
    "print(result)\n",
    "print()\n",
    "print(\"说明：\")\n",
    "print(\"- 1月1日、1月2日：只有ts1有值，结果为NaN\")\n",
    "print(\"- 1月3日-1月5日：两个序列都有值，正常相加\")\n",
    "print(\"- 1月6日、1月7日：只有ts2有值，结果为NaN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第六部分：时间序列可视化\n",
    "\n",
    "时间序列数据非常适合可视化，可以直观地观察趋势、周期性和异常值。\n",
    "\n",
    "### 6.1 单个时间序列可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 合并为 DataFrame\n",
    "\n",
    "将多个时间序列合并为 DataFrame 可以方便地进行对比和分析。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "合并为 DataFrame:\n",
      "======================================================================\n",
      "            序列1   序列2    总和     乘积\n",
      "2023-01-01  1.0   NaN   NaN    NaN\n",
      "2023-01-02  2.0   NaN   NaN    NaN\n",
      "2023-01-03  3.0  10.0  13.0   30.0\n",
      "2023-01-04  4.0  20.0  24.0   80.0\n",
      "2023-01-05  5.0  30.0  35.0  150.0\n",
      "2023-01-06  NaN  40.0   NaN    NaN\n",
      "2023-01-07  NaN  50.0   NaN    NaN\n"
     ]
    }
   ],
   "source": [
    "# 合并为 DataFrame\n",
    "df = pd.DataFrame({\n",
    "    '序列1': ts1,\n",
    "    '序列2': ts2,\n",
    "    '总和': ts1 + ts2,\n",
    "    '乘积': ts1 * ts2\n",
    "})\n",
    "\n",
    "print(\"合并为 DataFrame:\")\n",
    "print(\"=\" * 70)\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/2073385519.py:11: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/2073385519.py:11: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/2073385519.py:11: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/2073385519.py:11: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/2073385519.py:11: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/2073385519.py:11: UserWarning: Glyph 24207 (\\N{CJK UNIFIED IDEOGRAPH-5E8F}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/2073385519.py:11: UserWarning: Glyph 21015 (\\N{CJK UNIFIED IDEOGRAPH-5217}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/2073385519.py:11: UserWarning: Glyph 31034 (\\N{CJK UNIFIED IDEOGRAPH-793A}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/2073385519.py:11: UserWarning: Glyph 20363 (\\N{CJK UNIFIED IDEOGRAPH-4F8B}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 24207 (\\N{CJK UNIFIED IDEOGRAPH-5E8F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21015 (\\N{CJK UNIFIED IDEOGRAPH-5217}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 31034 (\\N{CJK UNIFIED IDEOGRAPH-793A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20363 (\\N{CJK UNIFIED IDEOGRAPH-4F8B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 24207 (\\N{CJK UNIFIED IDEOGRAPH-5E8F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21015 (\\N{CJK UNIFIED IDEOGRAPH-5217}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 31034 (\\N{CJK UNIFIED IDEOGRAPH-793A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20363 (\\N{CJK UNIFIED IDEOGRAPH-4F8B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "单个时间序列可视化完成\n"
     ]
    }
   ],
   "source": [
    "# 创建示例时间序列数据\n",
    "dates = pd.date_range('2023-01-01', periods=100, freq='D')\n",
    "ts = pd.Series(np.random.randn(100).cumsum(), index=dates)\n",
    "\n",
    "# 基本绘图\n",
    "plt.figure(figsize=(12, 6))\n",
    "ts.plot(title='时间序列示例', linewidth=2)\n",
    "plt.xlabel('日期', fontsize=12)\n",
    "plt.ylabel('值', fontsize=12)\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"单个时间序列可视化完成\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第七部分：实际应用案例\n",
    "\n",
    "### 7.1 股票价格数据分析\n",
    "\n",
    "模拟股票价格数据，展示时间序列在实际数据分析中的应用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 多个时间序列可视化\n",
    "\n",
    "可以在同一张图中绘制多个时间序列，方便对比分析。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 22810 (\\N{CJK UNIFIED IDEOGRAPH-591A}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 20010 (\\N{CJK UNIFIED IDEOGRAPH-4E2A}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 24207 (\\N{CJK UNIFIED IDEOGRAPH-5E8F}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 21015 (\\N{CJK UNIFIED IDEOGRAPH-5217}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 23545 (\\N{CJK UNIFIED IDEOGRAPH-5BF9}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 24207 (\\N{CJK UNIFIED IDEOGRAPH-5E8F}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/8127646.py:15: UserWarning: Glyph 21015 (\\N{CJK UNIFIED IDEOGRAPH-5217}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 22810 (\\N{CJK UNIFIED IDEOGRAPH-591A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20010 (\\N{CJK UNIFIED IDEOGRAPH-4E2A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 24207 (\\N{CJK UNIFIED IDEOGRAPH-5E8F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21015 (\\N{CJK UNIFIED IDEOGRAPH-5217}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 23545 (\\N{CJK UNIFIED IDEOGRAPH-5BF9}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 22810 (\\N{CJK UNIFIED IDEOGRAPH-591A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20010 (\\N{CJK UNIFIED IDEOGRAPH-4E2A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 24207 (\\N{CJK UNIFIED IDEOGRAPH-5E8F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21015 (\\N{CJK UNIFIED IDEOGRAPH-5217}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 23545 (\\N{CJK UNIFIED IDEOGRAPH-5BF9}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "多个时间序列可视化完成\n"
     ]
    }
   ],
   "source": [
    "# 创建多个时间序列\n",
    "df = pd.DataFrame({\n",
    "    '序列1': np.random.randn(100).cumsum(),\n",
    "    '序列2': np.random.randn(100).cumsum(),\n",
    "    '序列3': np.random.randn(100).cumsum()\n",
    "}, index=dates)\n",
    "\n",
    "# 绘制多个序列\n",
    "plt.figure(figsize=(12, 6))\n",
    "df.plot(title='多个时间序列对比', linewidth=2)\n",
    "plt.xlabel('日期', fontsize=12)\n",
    "plt.ylabel('值', fontsize=12)\n",
    "plt.legend(loc='best')\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"多个时间序列可视化完成\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 21644 (\\N{CJK UNIFIED IDEOGRAPH-548C}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 31227 (\\N{CJK UNIFIED IDEOGRAPH-79FB}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 21160 (\\N{CJK UNIFIED IDEOGRAPH-52A8}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 22343 (\\N{CJK UNIFIED IDEOGRAPH-5747}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "股票数据示例（前10条）:\n",
      "======================================================================\n",
      "                 price   returns  volume\n",
      "2023-01-02  101.093428  0.010934    7617\n",
      "2023-01-03  100.914969 -0.001765    3479\n",
      "2023-01-04  102.323114  0.013954    7484\n",
      "2023-01-05  105.542260  0.031461    8421\n",
      "2023-01-06  105.153541 -0.003683    5452\n",
      "2023-01-09  104.766288 -0.003683    6881\n",
      "2023-01-10  108.180019  0.032584    7665\n",
      "2023-01-11  109.948621  0.016349    3849\n",
      "2023-01-12  109.026209 -0.008389    8390\n",
      "2023-01-13  110.318300  0.011851    7905\n",
      "\n",
      "数据总天数: 260 个交易日\n",
      "价格范围: 82.18 - 128.47\n",
      "\n",
      "添加移动平均线后（后10条）:\n",
      "======================================================================\n",
      "                 price       ma_20       ma_50\n",
      "2023-12-18  118.177764  119.170878  117.243993\n",
      "2023-12-19  120.465359  119.284115  117.607317\n",
      "2023-12-20  125.698751  119.647725  118.028945\n",
      "2023-12-21  128.420041  120.218645  118.458808\n",
      "2023-12-22  124.646110  120.344173  118.869977\n",
      "2023-12-25  123.563599  120.332045  119.296461\n",
      "2023-12-26  126.818044  120.727174  119.764927\n",
      "2023-12-27  125.149957  121.010781  120.176713\n",
      "2023-12-28  126.385987  121.429284  120.589588\n",
      "2023-12-29  128.470431  121.845507  120.879078\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 22343 (\\N{CJK UNIFIED IDEOGRAPH-5747}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/var/folders/9b/jb3nnnqn60x3dtlll6mcf0000000gp/T/ipykernel_74174/3569440580.py:38: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21644 (\\N{CJK UNIFIED IDEOGRAPH-548C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 31227 (\\N{CJK UNIFIED IDEOGRAPH-79FB}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21160 (\\N{CJK UNIFIED IDEOGRAPH-52A8}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 22343 (\\N{CJK UNIFIED IDEOGRAPH-5747}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20215 (\\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26684 (\\N{CJK UNIFIED IDEOGRAPH-683C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21644 (\\N{CJK UNIFIED IDEOGRAPH-548C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 31227 (\\N{CJK UNIFIED IDEOGRAPH-79FB}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21160 (\\N{CJK UNIFIED IDEOGRAPH-52A8}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 22343 (\\N{CJK UNIFIED IDEOGRAPH-5747}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "股票价格分析图表已生成\n"
     ]
    }
   ],
   "source": [
    "# 模拟股票价格数据\n",
    "np.random.seed(42)\n",
    "dates = pd.date_range('2023-01-01', '2023-12-31', freq='B')  # 工作日（Business day）\n",
    "returns = np.random.normal(0.001, 0.02, len(dates))  # 日收益率\n",
    "prices = 100 * (1 + returns).cumprod()  # 价格序列（从100开始）\n",
    "\n",
    "stock_data = pd.DataFrame({\n",
    "    'price': prices,\n",
    "    'returns': returns,\n",
    "    'volume': np.random.randint(1000, 10000, len(dates))\n",
    "}, index=dates)\n",
    "\n",
    "print(\"股票数据示例（前10条）:\")\n",
    "print(\"=\" * 70)\n",
    "print(stock_data.head(10))\n",
    "print()\n",
    "print(f\"数据总天数: {len(stock_data)} 个交易日\")\n",
    "print(f\"价格范围: {stock_data['price'].min():.2f} - {stock_data['price'].max():.2f}\")\n",
    "print()\n",
    "\n",
    "# 计算移动平均线\n",
    "stock_data['ma_20'] = stock_data['price'].rolling(20).mean()  # 20日移动平均\n",
    "stock_data['ma_50'] = stock_data['price'].rolling(50).mean()  # 50日移动平均\n",
    "\n",
    "print(\"添加移动平均线后（后10条）:\")\n",
    "print(\"=\" * 70)\n",
    "print(stock_data[['price', 'ma_20', 'ma_50']].tail(10))\n",
    "print()\n",
    "\n",
    "# 绘制价格和移动平均线\n",
    "plt.figure(figsize=(15, 8))\n",
    "stock_data[['price', 'ma_20', 'ma_50']].plot(linewidth=2)\n",
    "plt.title('股票价格和移动平均线', fontsize=14, fontweight='bold')\n",
    "plt.xlabel('日期', fontsize=12)\n",
    "plt.ylabel('价格', fontsize=12)\n",
    "plt.legend(['价格', '20日均线', '50日均线'], fontsize=11)\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"股票价格分析图表已生成\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2 时间序列查询示例\n",
    "\n",
    "使用时间索引进行灵活的数据查询。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "时间序列查询示例:\n",
      "======================================================================\n",
      "\n",
      "1. 查询2023年3月的数据:\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'2023-03'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/pandas/core/indexes/base.py:3812\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3811\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3812\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   3813\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:167\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:196\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7096\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[31mKeyError\u001b[39m: '2023-03'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[16]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m      5\u001b[39m \u001b[38;5;66;03m# 查询特定月份\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m1. 查询2023年3月的数据:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[43mstock_data\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m2023-03\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m[[\u001b[33m'\u001b[39m\u001b[33mprice\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mvolume\u001b[39m\u001b[33m'\u001b[39m]].head())\n\u001b[32m      8\u001b[39m \u001b[38;5;28mprint\u001b[39m()\n\u001b[32m     10\u001b[39m \u001b[38;5;66;03m# 查询特定季度\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/pandas/core/frame.py:4113\u001b[39m, in \u001b[36mDataFrame.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   4111\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.columns.nlevels > \u001b[32m1\u001b[39m:\n\u001b[32m   4112\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._getitem_multilevel(key)\n\u001b[32m-> \u001b[39m\u001b[32m4113\u001b[39m indexer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   4114\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[32m   4115\u001b[39m     indexer = [indexer]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/envs/ml311/lib/python3.11/site-packages/pandas/core/indexes/base.py:3819\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3814\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   3815\u001b[39m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc.Iterable)\n\u001b[32m   3816\u001b[39m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[32m   3817\u001b[39m     ):\n\u001b[32m   3818\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[32m-> \u001b[39m\u001b[32m3819\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m   3820\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m   3821\u001b[39m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[32m   3822\u001b[39m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[32m   3823\u001b[39m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[32m   3824\u001b[39m     \u001b[38;5;28mself\u001b[39m._check_indexing_error(key)\n",
      "\u001b[31mKeyError\u001b[39m: '2023-03'"
     ]
    }
   ],
   "source": [
    "# 时间序列查询示例\n",
    "print(\"时间序列查询示例:\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "# 查询特定月份\n",
    "print(\"\\n1. 查询2023年3月的数据:\")\n",
    "print(stock_data['2023-03'][['price', 'volume']].head())\n",
    "print()\n",
    "\n",
    "# 查询特定季度\n",
    "print(\"2. 查询2023年第一季度（Q1）的数据:\")\n",
    "print(stock_data['2023-01':'2023-03'][['price']].head())\n",
    "print()\n",
    "\n",
    "# 查询特定日期范围\n",
    "print(\"3. 查询1月1日到1月10日的数据:\")\n",
    "print(stock_data['2023-01-01':'2023-01-10'][['price', 'volume']])\n",
    "print()\n",
    "\n",
    "# 统计信息\n",
    "print(\"4. 月度统计信息（1月）:\")\n",
    "monthly_stats = stock_data['2023-01'].agg({\n",
    "    'price': ['mean', 'min', 'max'],\n",
    "    'volume': 'sum'\n",
    "})\n",
    "print(monthly_stats)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "\n",
    "### 核心要点回顾\n",
    "\n",
    "1. **DatetimeIndex 基础**\n",
    "   - 使用 `pd.date_range()` 创建规则时间索引\n",
    "   - 从字符串列表创建不规则时间索引\n",
    "   - 了解 DatetimeIndex 的常用属性（freq、tz等）\n",
    "\n",
    "2. **创建时间序列**\n",
    "   - 使用 DatetimeIndex 作为 Series 或 DataFrame 的索引\n",
    "   - 时间序列支持所有标准的数据操作\n",
    "\n",
    "3. **时间索引和切片**\n",
    "   - 按完整日期索引：`ts['2023-01-01']`\n",
    "   - 按月份索引：`ts['2023-01']`\n",
    "   - 按年份索引：`ts['2023']`\n",
    "   - 日期范围切片：`ts['2023-01-01':'2023-01-10']`\n",
    "   - 使用 `loc` 和 `iloc` 进行索引\n",
    "\n",
    "4. **重新索引和填充**\n",
    "   - 使用 `reindex()` 转换不规则时间序列\n",
    "   - 前向填充（ffill）、后向填充（bfill）\n",
    "   - 线性插值（interpolate）\n",
    "\n",
    "5. **时间序列对齐**\n",
    "   - 不同时间范围的序列运算时自动对齐\n",
    "   - 合并多个时间序列为 DataFrame\n",
    "\n",
    "6. **可视化**\n",
    "   - 使用 `plot()` 方法绘制时间序列\n",
    "   - 支持单个和多个序列的可视化\n",
    "\n",
    "### 常用技巧\n",
    "\n",
    "- **频率字符串**：'D'（日）、'B'（工作日）、'W'（周）、'M'（月）、'Q'（季度）、'Y'（年）\n",
    "- **部分字符串索引**：可以使用年份、月份等部分字符串进行索引\n",
    "- **自动对齐**：时间序列运算时自动对齐到共同索引\n",
    "- **缺失值处理**：根据业务需求选择合适的填充方法\n",
    "\n",
    "### 扩展学习\n",
    "\n",
    "- 时间序列重采样：`resample()` 方法\n",
    "- 时间序列属性提取：年、月、日、星期等\n",
    "- 时间序列运算：时间差、日期偏移等\n",
    "- 时区处理：`tz_localize()` 和 `tz_convert()`\n"
   ]
  }
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